Detecting Behavioural Motifs of Mosquitoes Using Deep Variational Embedding of Posture Dynamics
| dc.contributor.advisor | Hol, Felix | |
| dc.contributor.advisor | Ambrogioni, Luca | |
| dc.contributor.advisor | Kwisthout, Johan | |
| dc.contributor.author | Shahbaaz Khan, Ali | |
| dc.date.issued | 2023-10-01 | |
| dc.description.abstract | Mosquitoes are considered to be one of the most dangerous animals as they have been responsible for countless number of deaths in the past. Even to this day, mosquito-borne diseases such as malaria and dengue continue to claim millions of lives in humid regions around the world. Recent advancements in the study of mosquito behavior have shed light on alterations in the behaviour of infected mosquitoes which facilitates the easier spread of diseases. The quantification of these behavioral alterations offers a promising avenue for the reduction in infection transmission. Investigating the genetic influence on behaviour could help establish correlations between specific genes and their impact on mosquito biting behaviors, potentially leading to the development of disease-resistant ”super mosquitoes” that cannot transmit infections to other animals. In the field of biology and medicine, deep learning approaches have helped scientists explore new avenues. From tumor detection to wildlife tracking, biologists can now explore novel theories and approaches. The combination of different machine learning methodologies to address complex problems has consistently yielded reliable results. Drawing inspiration from the success of deep learning, our work introduces a comprehensive pipeline using techniques such as pose estimation, tracking, dimensionality reduction, and clustering to quantify observed behavioural alterations in mosquitoes. This project introduces a methodology, wherein we utilize mosquito videos to recognise and analyze their behaviours. We compare the behaviors exhibited by a population of dengue-infected mosquitoes with those of an uninfected control population. Our primary focus lies in the identification of behaviours most crucial for disease transmission and ascertaining whether the distinctions between the control and dengue-infected mosquito populations align with recent findings in the field. | |
| dc.identifier.uri | https://theses.ubn.ru.nl/handle/123456789/16410 | |
| dc.language.iso | en | |
| dc.thesis.faculty | Faculteit der Sociale Wetenschappen | |
| dc.thesis.specialisation | specialisations::Faculteit der Sociale Wetenschappen::Artificial Intelligence::Master Artificial Intelligence | |
| dc.thesis.studyprogramme | studyprogrammes::Faculteit der Sociale Wetenschappen::Artificial Intelligence | |
| dc.thesis.type | Master | |
| dc.title | Detecting Behavioural Motifs of Mosquitoes Using Deep Variational Embedding of Posture Dynamics |
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